ケラスのベクトル間の距離と角度を計算するLSTM

Question

私はケラを使って質問のペア間の類似性を検出しています。モデル構造は正常に動作しているようですが、model.fit関数でエラーが発生します。私は入力データのデータ型をチェックし、numpy.ndarrayです。この点に関する指針と私は感謝します。

ValueError: Error when checking model input: the list of Numpy arrays that you are passing to your model is not the size the model expected. Expected to see 1 arrays but instead got the following list of 2 arrays: [array([[ 0, 0, 0, ..., 251, 46, 50], [ 0, 0, 0, ..., 7, 40, 6935], [ 0, 0, 0, ..., 17, 314, 2317], ..., [ 0, ...

def Angle(inputs): 

    length_input_1=K.sqrt(K.sum(tf.pow(inputs[0],2),axis=1,keepdims=True)) 
    length_input_2=K.sqrt(K.sum(tf.pow(inputs[1],2),axis=1,keepdims=True)) 
    result=K.batch_dot(inputs[0],inputs[1],axes=1)/(length_input_1*length_input_2) 
    angle = tf.acos(result) 
    return angle 

def Distance(inputs): 

    s = inputs[0] - inputs[1] 
    output = K.sum(s ** 2,axis=1,keepdims=True) 
    return output  




y=data.is_duplicate.values 
tk=text.Tokenizer() 
tk.fit_on_texts(list(data.question1.values)+list(data.question2.values)) 


question1 = tk.texts_to_sequences(data.question1.values) 
question1 = sequence.pad_sequences(question1,maxlen=MAX_LEN) 

question2 = tk.texts_to_sequences(data.question2.values) 
question2 = sequence.pad_sequences(question2,maxlen=MAX_LEN) 


word_index = tk.word_index 
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s', level=logging.INFO) 
num_features = 300 
num_workers = multiprocessing.cpu_count() 
context_size = 5 
downsampling = 7.5e-06 
seed = 1 
min_word_count = 5 
hs = 1 
negative = 5 


Quora_word2vec = gensim.models.Word2Vec(

    sg=0, 
    seed=1, 
    workers=num_workers, 
    min_count=min_word_count, 
    size=num_features, 
    window=context_size, # (2 and 5) 
    hs=hs, # (1 and 0) 
    negative=negative, # (5 and 10) 
    sample=downsampling # (range (0, 1e-5).) 

) 

Quora_word2vec = gensim.models.KeyedVectors.load_word2vec_format('GoogleNews-vectors-negative300.bin',binary=True) 
embedding_matrix=np.zeros((len(word_index)+1,300)) 

for word , i in tqdm(word_index.items()): #i is index 

    try: 

     embedding_vector = Quora_word2vec[word] #Exception is thrown if there is key error 
     embedding_matrix[i] = embedding_vector 

    except Exception as e: #If word is not found continue 

     continue 

-------- -------- question1

model1 = Sequential() 
print "Build Model" 

model1.add(Embedding(
    len(word_index)+1, 
    300, 
    weights=[embedding_matrix], 
    input_length=MAX_LEN 
    )) 

model1.add(SpatialDropout1D(0.2)) 
model1.add(TimeDistributed(Dense(300, activation='relu'))) 
model1.add(Lambda(lambda x: K.sum(x, axis=1), output_shape=(300,))) 

print model1.summary() 

＃--------- question2 ----- - ＃

model2=Sequential() 

model2.add(Embedding(
    len(word_index) + 1, 
    300, 
    weights=[embedding_matrix], 
    input_length=MAX_LEN 
    )) # Embedding layer 
model2.add(SpatialDropout1D(0.2)) 
model2.add(TimeDistributed(Dense(300, activation='relu'))) 
model2.add(Lambda(lambda x: K.sum(x, axis=1), output_shape=(300,))) 


print model2.summary() 


#---------Merged------# 

#Here you get question embedding 

#Calculate distance between vectors 
Distance_merged_model=Sequential() 
Distance_merged_model.add(Merge(layers=[model1, model2], mode=Distance, output_shape=(1,))) 

print Distance_merged_model.summary() 

#Calculate Angle between vectors 

Angle_merged_model=Sequential() 
Angle_merged_model.add(Merge(layers=[model1,model2],mode=Angle,output_shape=(1,))) 

print Angle_merged_model.summary() 


neural_network = Sequential() 
neural_network.add(Dense(2,input_shape=(1,))) 
neural_network.add(Dense(1)) 
neural_network.add(Activation('sigmoid')) 

print neural_network.summary() 


neural_network.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) 

checkpoint = ModelCheckpoint('weights.h5', monitor='val_acc', save_best_only=True, verbose=2) 

print type(question1) 
print type(question2) 
neural_network.fit([question1,question2],y=y, batch_size=384, epochs=10, 
       verbose=1, validation_split=0.3, shuffle=True, callbacks=[checkpoint]) 

Answer 1

あなたが密にあなたの最後の二つの層を接続し、DISを持ってしてその層でのコンパイルとフィッティングされているので、ちょうどあなたのニューラルネットワークはに渡される唯一のネットワーク持つデータも持っていませんあなたの最終的な密集度に接続しています。

ネットワーク＃1、＃2を取得してすべてがマージ層に正しいように見えるが、あなたはに似た何かをする必要があります。

neural_network = Sequential() 
neural_network.add(Merge([Distance_merged_model, Angle_merged_model], mode='concat')) 
neural_network.add(Dense(2,input_shape=(1,))) 
neural_network.add(Dense(1)) 
neural_network.add(Activation('sigmoid'))